This work introduces the definition of observation-specific explanations to
assign a score to each data point proportional to its importance in the
definition of the prediction process. Such explanations involve the
identification of the most influential observations for the black-box model of
interest. The proposed method involves estimating these explanations by
constructing a surrogate model through scattered data approximation utilizing
the orthogonal matching pursuit algorithm. The proposed approach is validated
on both simulated and real-world datasets.

本研究介绍了观测特定解释的定义，通过为每个数据点分配与其在预测过程中的重要性成比例的分数。这种解释涉及对感兴趣的黑匣子模型中最具影响力的观测的识别。通过利用正交匹配追踪算法构建散乱数据逼近的代理模型来估计这些解释的建议方法。该方法在模拟和真实世界数据集上得到验证。

基于散乱数据逼近的观测特定解释

Observation-specific explanations through scattered data approximation

In many scenarios, one uses a large training set to train a model with the
goal of performing well on a smaller testing set with a different distribution.
Learning a weight for each data point of the training set is an appealing
solution, as it ideally allows one to automatically learn the importance of
each training point for generalization on the testing set. This task is usually
formalized as a bilevel optimization problem. Classical bilevel solvers are
based on a warm-start strategy where both the parameters of the models and the
data weights are learned at the same time. We show that this joint dynamic may
lead to sub-optimal solutions, for which the final data weights are very
sparse. This finding illustrates the difficulty of data reweighting and offers
a clue as to why this method is rarely used in practice.

利用大训练集训练模型以在不同分布的较小测试集上表现良好的任务中，通过为每个训练数据点学习权重是一种吸引人的解决方案，但传统的双层优化算法可能导致次优解，这表明了数据重新加权的困难，并解释了为何这种方法很少在实践中使用。

使用双层优化重新加权数据的挑战

A Challenge in Reweighting Data with Bilevel Optimization

A trained neural network model contains information on the training data.
Given such a model, malicious parties can leverage the "knowledge" in this
model and design ways to print out any usable information (known as model
inversion attack). Therefore, it is valuable to explore the ways to conduct a
such attack and demonstrate its severity. In this work, we proposed ways to
generate a data point of the target class without prior knowledge of the exact
target distribution by using a pre-trained diffusion model.

该研究提出利用预训练扩散模型生成目标类别的数据点的方法，突破先前攻击者需要准确的先验目标分布的限制，以此发动神经网络模型逆推攻击的严重性。